Embodiments of the present specification relate to imaging, and more particularly to estimation of image intensity bias and segmentation of tissue classes.
In modern healthcare facilities, non-invasive imaging systems are often used for identifying, diagnosing, and treating physical conditions. Medical imaging encompasses different techniques used to image and visualize the internal structures and/or functional behavior (such as chemical or metabolic activity) of organs and tissues within a patient. Currently, a number of modalities of medical diagnostic and imaging systems exist, each typically operating on different physical principles to generate different types of images and information. These modalities include ultrasound systems, computed tomography (CT) systems, X-ray systems (including both conventional and digital or digitized imaging systems), positron emission tomography (PET) systems, single photon emission computed tomography (SPECT) systems, and magnetic resonance (MR) imaging systems.
Over the last few years, use of PET-MR imaging has been gaining momentum. In particular, significant technical advancements have enabled integration of PET and MR imaging solutions. However, MR signals, despite use of multiple contrasts, fail to correlate with PET photon attenuation. Therefore, MR image analysis methods in the form of segmentation of fat-water Dixon images (for example, thresholding based methods, active contour methods, and phase field based methods) and atlas/template registration have been investigated to generate attenuation correction (AC) maps based on tissue classification. Phase-field based methods are similar to the active contour methods. In particular, the phase-field methods provide a closed contour solution and are resilient to image noise when compared to thresholding based methods. However, the phase-field based tissue classification needs to be “tuned” to account for non-homogenous signal intensity distribution across whole body MR images. The inhomogeneity in the signal intensity in the MR images is primarily attributed to radio frequency (RF) transmission and coil sensitivity bias.
Problems associated with the inhomogeneity in the MR images may be substantially mitigated via use of body coil based image data acquisition. Moreover, since the phase-field based methods are relatively insensitive to image signal to noise ratio (SNR), higher encoding efficiency may be achieved via use of surface coil based parallel imaging methods. Also, surface coil based single-breathhold acquisition of high-resolution images may be employed for AC map generation and for anatomical referencing of PET findings. However, use of surface coil based image data acquisition results in large intensity bias in the MR image signal data. Traditional segmentation methods perform poorly in the presence of the large intensity signal bias. Moreover, the coil sensitivity related spatial signal variations associated with surface coil based image data acquisition exacerbate the need for retuning the segmentation techniques. Additionally, these coil sensitivity related spatial signal variations result in tissue segmentation failures even in cases of moderate shading.